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This is a guest post by Randy Zwitch (@randyzwitch), a digital analytics and predictive modeling consultant in the Greater Philadelphia area. Randy blogs regularly about Data Science and related technologies at http://randyzwitch.com.

A few months ago I passed the 10-year point in my analytics/predictive modeling career. While ‘Big Data’ and ‘Data Science’ have only become buzzwords in recent years, hitting the limit on computing resources has been something that has plagued me throughout my career. I’ve seen this problem manifest itself in many ways in all types of companies from sites where you can buy YouTube plays to large corporations. The problem ranges having analysts get assigned multiple computers for daily work, to continuously scraping together budget for more processors on a remote SAS server and spending millions on large enterprise databases just to get processing of data below a 24-hour window.

Luckily, advances in open source software & cloud computing have driven down the cost of data processing & analysis immensely. Using IPython Notebook along with Amazon EC2, you can now procure a 32-core, 60GB RAM virtual machine for roughly $0.27/hr (using a spot instance). This tutorial will show you how to setup a cluster instance at Amazon, install Python, setup IPython as a public notebook server and access this remote cluster via your local web browser.

To get started with this tutorial, you need to have an Amazon Web Services account. I also assume that you already have basic experience interacting with computers via the command line and know about IPython. Basically, that you are the average Bad Hessian reader…